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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2202

Title: Applying linear mixed models to estimate reliability in clinical trial data with repeated measurements
LAENEN, Annouschka
GEYS, Helena
RENARD, Didier
Issue Date: 2004
Citation: CONTROLLED CLINICAL TRIALS, 25(1). p. 13-30
Abstract: Repeated measures are exploited to study reliability in the context of psychiatric health sciences. It is shown how test-retest reliability can be derived using linear mixed models when the scale is continuous or quasi-continuous. The advantage of this approach is that the full modeling power of mixed models can be used. Repeated measures with a different mean structure can be used to usefully study reliability, correction for covariate effects is possible, and a complicated variance - covariance structure between measurements is allowed. In case the variance structure reduces to a random intercept (compound symmetry), classical methods are recovered. With more complex variance structures (e.g., including random slopes of time and/or serial correlation), time-dependent reliability functions are obtained. The methodology is motivated by and applied to data from five double-blind randomized clinical trials comparing the effects of risperidone to conventional antipsychotic agents for the treatment of chronic schizophrenia. Model assumptions are investigated through residual plots and by investigating the effect of influential observations. (C) 2004 Elsevier Inc. All rights reserved.
Notes: Johnson & Johnson Pharmaceut Res & Dev, Beerse, Belgium. Limburgs Univ Ctr, TUL, Ctr Stat, Diepenbeek, Belgium. Eli Lilly & Co, Mont St Guibert, Belgium.Vangeneugden, T, Janssen Pharmaceut, Turnhoutseweg 30, B-2340 Beerse, Belgium.tvangene@prdbe.jnj.com
URI: http://hdl.handle.net/1942/2202
DOI: 10.1016/j.cct.2003.08.009
ISI #: 000220636100002
ISSN: 0197-2456
Category: A1
Type: Journal Contribution
Validation: ecoom, 2005
Appears in Collections: Research publications

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